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Gradio Tools

There are many 1000s of Gradio apps on Hugging Face Spaces. This library puts them at the tips of your LLM's fingers 🦾

Specifically, gradio-tools is a Python library for converting Gradio apps into tools that can be leveraged by a large language model (LLM)-based agent to complete its task. For example, an LLM could use a Gradio tool to transcribe a voice recording it finds online and then summarize it for you. Or it could use a different Gradio tool to apply OCR to a document on your Google Drive and then answer questions about it.

It's very easy to create you own tool if you want to use a space that's not one of the pre-built tools. Please see this section of the gradio-tools documentation for information on how to do that. All contributions are welcome!

# !pip install gradio_tools

Using a tool​

from gradio_tools.tools import StableDiffusionTool
local_file_path = StableDiffusionTool().langchain.run(
    "Please create a photo of a dog riding a skateboard"
)
local_file_path
    Loaded as API: https://gradio-client-demos-stable-diffusion.hf.space ✔
    
    Job Status: Status.STARTING eta: None





    '/Users/harrisonchase/workplace/langchain/docs/modules/agents/tools/integrations/b61c1dd9-47e2-46f1-a47c-20d27640993d/tmp4ap48vnm.jpg'
from PIL import Image
im = Image.open(local_file_path)
display(im)
    
![png](_gradio_tools_files/output_7_0.png)

Using within an agent​

from langchain.agents import initialize_agent
from langchain.llms import OpenAI
from gradio_tools.tools import (
    StableDiffusionTool,
    ImageCaptioningTool,
    StableDiffusionPromptGeneratorTool,
    TextToVideoTool,
)

from langchain.memory import ConversationBufferMemory

llm = OpenAI(temperature=0)
memory = ConversationBufferMemory(memory_key="chat_history")
tools = [
    StableDiffusionTool().langchain,
    ImageCaptioningTool().langchain,
    StableDiffusionPromptGeneratorTool().langchain,
    TextToVideoTool().langchain,
]


agent = initialize_agent(
    tools, llm, memory=memory, agent="conversational-react-description", verbose=True
)
output = agent.run(
    input=(
        "Please create a photo of a dog riding a skateboard "
        "but improve my prompt prior to using an image generator."
        "Please caption the generated image and create a video for it using the improved prompt."
    )
)
    Loaded as API: https://gradio-client-demos-stable-diffusion.hf.space ✔
    Loaded as API: https://taesiri-blip-2.hf.space ✔
    Loaded as API: https://microsoft-promptist.hf.space ✔
    Loaded as API: https://damo-vilab-modelscope-text-to-video-synthesis.hf.space ✔
    
    
    > Entering new AgentExecutor chain...
    
    Thought: Do I need to use a tool? Yes
    Action: StableDiffusionPromptGenerator
    Action Input: A dog riding a skateboard
    Job Status: Status.STARTING eta: None
    
    Observation: A dog riding a skateboard, digital painting, artstation, concept art, smooth, sharp focus, illustration, art by artgerm and greg rutkowski and alphonse mucha
    Thought: Do I need to use a tool? Yes
    Action: StableDiffusion
    Action Input: A dog riding a skateboard, digital painting, artstation, concept art, smooth, sharp focus, illustration, art by artgerm and greg rutkowski and alphonse mucha
    Job Status: Status.STARTING eta: None
    
    Job Status: Status.PROCESSING eta: None
    
    Observation: /Users/harrisonchase/workplace/langchain/docs/modules/agents/tools/integrations/2e280ce4-4974-4420-8680-450825c31601/tmpfmiz2g1c.jpg
    Thought: Do I need to use a tool? Yes
    Action: ImageCaptioner
    Action Input: /Users/harrisonchase/workplace/langchain/docs/modules/agents/tools/integrations/2e280ce4-4974-4420-8680-450825c31601/tmpfmiz2g1c.jpg
    Job Status: Status.STARTING eta: None
    
    Observation: a painting of a dog sitting on a skateboard
    Thought: Do I need to use a tool? Yes
    Action: TextToVideo
    Action Input: a painting of a dog sitting on a skateboard
    Job Status: Status.STARTING eta: None
    Due to heavy traffic on this app, the prediction will take approximately 73 seconds.For faster predictions without waiting in queue, you may duplicate the space using: Client.duplicate(damo-vilab/modelscope-text-to-video-synthesis)
    
    Job Status: Status.IN_QUEUE eta: 73.89824726581574
    Due to heavy traffic on this app, the prediction will take approximately 42 seconds.For faster predictions without waiting in queue, you may duplicate the space using: Client.duplicate(damo-vilab/modelscope-text-to-video-synthesis)
    
    Job Status: Status.IN_QUEUE eta: 42.49370198879602
    
    Job Status: Status.IN_QUEUE eta: 21.314297944849187
    
    Observation: /var/folders/bm/ylzhm36n075cslb9fvvbgq640000gn/T/tmp5snj_nmzf20_cb3m.mp4
    Thought: Do I need to use a tool? No
    AI: Here is a video of a painting of a dog sitting on a skateboard.
    
    > Finished chain.